Papers Tropical Cyclone Intensity Forecasting
“Tropical Cyclone Intensity Forecasting” 태그가 달린 논문 5편 · 필터 해제
Physics-Informed Residual Neural Ordinary Differential Equations for Enhanced Tropical Cyclone Intensity Forecasting
Accurate tropical cyclone (TC) intensity prediction is crucial for mitigating storm hazards, yet its complex dynamics pose challenges to traditional methods. Here, we introduce a Physics-Informed Residual Neural Ordinary…
Deep LearningTropical Cyclone Intensity ForecastingVQLTI: Long-Term Tropical Cyclone Intensity Forecasting with Physical Constraints
Tropical cyclone (TC) intensity forecasting is crucial for early disaster warning and emergency decision-making. Numerous researchers have explored deep-learning methods to address computational and post-processing issue…
Tropical Cyclone Intensity ForecastingGlobal Tropical Cyclone Intensity Forecasting with Multi-modal Multi-scale Causal Autoregressive Model
Accurate forecasting of Tropical cyclone (TC) intensity is crucial for formulating disaster risk reduction strategies. Current methods predominantly rely on limited spatiotemporal information from ERA5 data and neglect t…
Tropical Cyclone Intensity ForecastingEnsemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach
Accurate time series forecasting is critical for a wide range of problems with temporal data. Ensemble modeling is a well-established technique for leveraging multiple predictive models to increase accuracy and robustnes…
ManagementTime SeriesTime Series ForecastingTropical Cyclone Intensity ForecastingHurricane Forecasting: A Novel Multimodal Machine Learning Framework
This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hur…
BIG-bench Machine LearningDecoderHurricane ForecastingTropical Cyclone Intensity Forecasting+1